A large number of studies on social media compare the behaviour of users from
different political parties. As a basic step, they employ a predictive model
for inferring their political affiliation. The accuracy of this model can
change the conclusions of a downstream analysis significantly, yet the choice
between different models seems to be made arbitrarily. In this paper, we
provide a comprehensive survey and an empirical comparison of the current party
prediction practices and propose several new approaches which are competitive
with or outperform state-of-the-art methods, yet require less computational
resources. Party prediction models rely on the content generated by the users
(e.g., tweet texts), the relations they have (e.g., who they follow), or their
activities and interactions (e.g., which tweets they like). We examine all of
these and compare their signal strength for the party prediction task. This
paper lets the practitioner select from a wide range of data types that all
give strong performance. Finally, we conduct extensive experiments on different
aspects of these methods, such as data collection speed and transfer
capabilities, which can provide further insights for both applied and
methodological research.

通过综合调查和实证比较当前党派预测实践，本文提出了几种与或超过最先进方法相竞争的新方法，同时需要更少的计算资源，使从业者能够从多种数据类型中选择并获得强大性能。

Twitter 党派预测

Party Prediction for Twitter

This paper assesses the accuracy, reliability and bias of the Large Language
Model (LLM) ChatGPT-4 on the text analysis task of classifying the political
affiliation of a Twitter poster based on the content of a tweet. The LLM is
compared to manual annotation by both expert classifiers and crowd workers,
generally considered the gold standard for such tasks. We use Twitter messages
from United States politicians during the 2020 election, providing a ground
truth against which to measure accuracy. The paper finds that ChatGPT-4 has
achieves higher accuracy, higher reliability, and equal or lower bias than the
human classifiers. The LLM is able to correctly annotate messages that require
reasoning on the basis of contextual knowledge, and inferences around the
author's intentions - traditionally seen as uniquely human abilities. These
findings suggest that LLM will have substantial impact on the use of textual
data in the social sciences, by enabling interpretive research at a scale.

本文研究了大型语言模型 ChatGPT-4 在 Twitter 文本分析任务中对发布者政治意向分类的精度、可靠性和偏差，并结论该模型精度更高、可靠性更高、偏差相等或更低，从而证明了该模型在社会科学中的巨大影响。

ChatGPT-4 在使用零样本学习对政治 Twitter 信息进行注释方面胜过专家和众包工作者

ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political  Twitter Messages with Zero-Shot Learning

Computational Politics is the study of computational methods to analyze and
moderate users' behaviors related to political activities such as election
campaign persuasion, political affiliation, and opinion mining. With the rapid
development and ease of access to the Internet, Information Communication
Technologies (ICT) have given rise to massive numbers of users joining online
communities and the digitization of analogous data such as political debates.
These communities and digitized data contain both explicit and latent
information about users and their behaviors related to politics. For
researchers, it is essential to utilize data from these sources to develop and
design systems that not only provide solutions to computational politics but
also help other businesses, such as marketers to increase users, participation
and interactions. In this survey, we attempt to categorize main areas in
computational politics and summarize the prominent studies in one place to
better understand computational politics across different and multidimensional
platforms. e.g., online social networks, online forums, and political debates.
We then conclude this study by highlighting future research directions,
opportunities, and challenges.

本研究对计算政治学的主要研究领域进行了分类，并总结了在不同的多维平台上的显著研究，以更好地理解计算政治学。然后，强调了未来的研究方向、机遇和挑战。